Search Results (200)

This study analyzed the impact of human drivers’ Keep Right Rule noncompliance on sustainable freeway operations in mixed traffic. Using the microscopic traffic simulation tool, a total of 36 scenarios were examined based on variations in driving behavior, presence of slow vehicles in the passing lane, desired speed, and number of lanes. The Wiedemann-99 car-following model and autonomous driving logic were applied for simulation. Simulation results revealed that the occupation of the passing lane by a human-driven slow vehicle increased the recovery time and variability in right-side rule compared to free lane selection. Also, 20 km/h was a threshold desired speed gap that activated the bottleneck by the slow vehicle in a passing lane. Lastly, as the number of lanes increased, bottleneck formation was diminished. The findings point to a mixed traffic systemic paradox. Human drivers can alleviate bottleneck formation by flexibly performing right-side overtaking even though it is illegal, whereas autonomous vehicles cannot perform right-side overtaking, which unintentionally activates a bottleneck under strict rule compliance. These results show that in mixed traffic conditions, even minor violations of traffic rules by human drivers can lead to congestion. Therefore, to achieve sustainable and safe road traffic by harmonizing mixed traffic, institutional improvements are necessary alongside advances in autonomous driving technology. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

As part of transportation planning processes, simulators are used to mirror real-world situations to test new policies and evaluate infrastructure changes. In practice, simulator selection has often been based on availability rather than on technical suitability, particularly for microscopic-scale applications. In this study, a quantitative methodology focusing on simulation runtime, memory usage, runtime consistency, travel time, safe gap distance, and scalability is proposed. A combined index was developed to assess simulators across different scales and traffic densities. VISSIM, SUMO, and MATSim were tested, and the results indicate that SUMO and MATSim demonstrate strong performance in runtime and memory usage. In large-scale scenarios, both simulators proved suitable for high-demand simulations, with MATSim exhibiting greater scalability. VISSIM matches real-world travel times more closely and fairly handles realistic safe gap distances, making it more suitable for less dense, detailed, microscopic simulations. Full article

As autonomous vehicles proliferate in mixed traffic streams, heterogeneous flows comprising vehicles with diverse driving strategies introduce significant complexity to cascading conflict propagation, while conventional conflict risk assessment methods based on homogeneous assumptions fail to capture the intricate risk transmission mechanisms embedded in high-dimensional trajectory data. To address the challenge, this study establishes a systematic data analytics framework. Firstly, a conflict risk quantification model is proposed by integrating safety field theory considering heterogeneity traffic flow, achieving precise quantification of microscopic interaction risks through vehicle risk coefficients that characterize differential risk sensitivity across distinct driving strategies. Secondly, a cascading conflict identification algorithm is designed to extract cascading propagation chains from trajectory data. Thirdly, a method to analyze cascading conflict risk propagation is developed using CatBoost (v1.2.8), coupled with SHapley Additive ExPlanations interpretability analysis to systematically reveal the propagation mechanisms underlying cascading conflicts. Empirical findings indicate that primary conflict intensity and longitudinal relative speed are the dominant predictive features for secondary conflicts; moreover, local traffic heterogeneity entropy exerts a significant moderating effect—quantitative analysis reveals that higher heterogeneity increases the likelihood of secondary conflicts under identical primary risk conditions. Comprehensive validation using SUMO microscopic simulation demonstrates that the proposed data analytics pipeline effectively identifies and accurately predicts and analyzes secondary conflicts across diverse traffic scenarios. This framework provides interpretable foundations for intelligent conflict-risk identification systems, propagation-mechanism analysis, and proactive safety interventions in heterogeneous traffic environments, offering significant implications for real-time traffic monitoring and intelligent transportation system design. Full article

Microscopic Particulate Matter (PM) below 10 µm can enter the respiratory system and affect human health in the short and long term. Railway enclosures are sites with high concentrations of fine PM and technical solutions like mechanical filtration exist to increase the air quality. However, several crucial factors must be evaluated and optimized like energy consumption, maintenance cost/interval, design and control. A fast and adaptable evaluation of decontamination solutions is required to find the optimal solution. To answer this, a 1D multizone model based on station discretization aligned with the track direction is proposed to precisely place decontamination systems along the station. In each zone, a set of ordinary differential equations is used to forecast the daily progression of PM concentrations, based on physical parameters (air and train velocities, and train traffic) used to describe the different physical phenomena (resuspension, deposition, ventilation and generation). Three-dimensional CFD (Computational Fluid Dynamics) simulations are used to characterize the efficiency and range of decontamination products and reproduce their effect in the 1D model. This approach allows for flexible optimization of local and global decontamination efficiencies with multiple parameter changes. PM₁₀ and PM_2.5 (below 10 and 2.5 µm) are studied here as they are often monitored. Full article

Urban intersections are critical nodes where traffic congestion and energy inefficiency converge. Traditional signal control systems often optimize either mobility or sustainability, creating an asymmetry between flow efficiency and environmental impact. This study introduces a symmetry-aware generative optimization framework that leverages Generative Artificial Intelligence (GAI) to balance both dimensions. Using the microscopic simulator SUMO, we modeled a signalized intersection in Colima, Mexico, under five control strategies: Fixed Time (baseline), GPT-4o, GPT-5 Thinking, Gemini 2.5 Pro, and DeepSeek V3. Each Large Language Model (LLM) received structured simulation data and generated new phase-duration configurations to minimize queue length, travel time, and CO₂ emissions while improving average speed. Step-level performance was evaluated using descriptive statistics, and Wilcoxon signed-rank tests paired with Holm–Bonferroni correction. Results show that all LLM-based controllers significantly outperformed the Fixed Time baseline (adjusted p ≤ 4.8 × 10⁻⁶), with large effect sizes (|dz| ≈ 1.5–2.6). GPT-5 achieved the strongest performance, reducing queue size by ≈ 44%, CO₂ emissions by ≈ 17%, and increasing average speed by ≈ 58%. The results validate the feasibility of symmetry-aware generative reasoning for sustainable traffic optimization and establish a reproducible methodological framework applicable to future AI-driven urban mobility systems. Full article

Advancements in technology and the emergence of vehicle-to-everything communication encourage new research approaches. Continuously sharing data through the onboard unit, connected vehicles (CVs) have proven to be a valuable source of real-time microscopic traffic data. Utilizing CVs as mobile sensors is a key driver for traffic safety improvement and increasing the effective operative road capacity. Data obtained from CVs can be effectively processed using speed transition matrices (STMs) while preserving spatial and temporal characteristics. This research proposes a new approach to adaptive traffic signal control utilizing STMs and a cooperative multi-agent learning system for the environment of CVs. To confirm its effectiveness, the concept is tested in a simulated environment of an intersection network, comparing different CVs’ penetration rates and cooperation coefficients between agents. Full article

This study investigated the impact of converting High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes on fundamental traffic flow characteristics, focusing on speed, density, and flow relationships. A 25-mile HOV corridor along I-24 Westbound in Nashville, Tennessee was evaluated using both microscopic simulation via VISSIM and data-driven machine learning through a Multi-Layer Perceptron (MLP) neural network. Four operational scenarios were assessed: (1) HOV lanes without enforcement, (2) HOV lanes with effective occupancy enforcement, (3) HOT lanes with limited access points, and (4) HOT lanes with intermediate access points. Flow-density and speed-flow relationships were modeled using Greenshields theory to extract key traffic performance thresholds including free-flow speed, jam density, and maximum flow. Results indicate that while free-flow speeds were generally consistent across scenarios (ranging from 71 to 80 mph), HOV and HOT lanes exhibited higher values compared to general-purpose lanes. Capacity increases were observed following HOV-to-HOT conversions, especially when intermediate access points were introduced. The MLP neural network successfully replicated nonlinear flow relationships and predicted maximum flow near 2000 vph with a jam density of approximately 215 vpmpl—values that closely matched simulation outputs. Both the VISSIM and MLP-derived diagrams demonstrated curve shapes and capacity thresholds that were highly consistent with Highway Capacity Manual (HCM) standards for freeway segments. However, slightly higher thresholds were observed for HOV/HOT lanes, suggesting their potential for improved operational performance under managed conditions. The integration of simulation and machine learning offers a robust framework for evaluating managed lane conversions and informing data-driven policy. Beyond the scenario-specific findings, the study demonstrates an innovative hybrid methodology that links detailed microsimulation with an explainable neural network model, providing a concise and scalable approach for analyzing managed-lane operations. This combined framework highlights the contribution of integrating simulation and AI to enhance the analytical depth and practical relevance of traffic flow studies. Full article

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management. Full article

Microscopic traffic simulation has a wide range of applications due to its high precision. However, the accuracy of such simulation is influenced by many factors during the simulation establishment process. This paper explores the impact of various factors on simulation results by comparing real-world traffic data, simulated data and simulations configured with different factors. The impact of these factors on simulation accuracy is evaluated by analyzing the traffic volume passing through a congested intersection in each direction. The results indicate that map correction, route iteration, and the inclusion of bus routes significantly affect simulation accuracy. An inaccurate map reduces traffic by 42%, while not-iterated routes prevent 6.6% of vehicles from using their original routes. Omitting bus routes increases the number of trips for private cars by 47%. Conversely, the inclusion of school zones has minimal impact, omitting them only reduces trips by 0.37%. Interestingly, integrating real traffic light data did not enhance simulation accuracy, likely due to discrepancies in junction turning percentages between the simulation and reality. This paper provides guidance for building accurate simulation maps using public data, enabling the creation of relatively precise models with minimal data and effort. Full article

Macroscopic fatigue tests, mesoscopic finite element simulations, and microscopic molecular dynamics simulations were composed to study the damage and failure of drainage asphalt mixtures in multiscale. The applicability of the fatigue models fit by strain, stress, and the linear fitting slope of the indirect tensile modulus curves were compared. The mesoscopic damage and failure distribution and evolution characteristics were studied, considering the single or coupling effect of traffic loading, hydrodynamic pressure, mortar aging, and interfacial attenuation. The microscopic molecular mechanism of the interface adhesion failure between the aggregate and mortar under water-containing conditions was analyzed. Results show that the fatigue model based on the linear fitting slopes of the indirect tensile modulus curves has significant applicability for drainage asphalt mixtures with different void rates and gradations. The damage and failure have an obvious leap development when traffic loading increases from 0.7 MPa to 0.8 MPa. The hydrodynamic pressure significantly increases the stress of the mortar around the voids and close to the aggregate, promoting damage development and crack extension, especially when it is greater than 0.3 MPa. With the aging deepening of the mortar, the increase rate of the damage degree gradually decreases from the top to the bottom of the mixture. With the development of interfacial attenuation, the damage and failure of interfaces continue increasing, while that of the mortar increases first and then decreases, which is related to the loading concentration in the interface and the stress decrease in the mortar. Under the coupling effects, whether the cracks mainly generate in the mortar or interface depends on their damage degrees, thus causing the stripping of the aggregate wrapped or not wrapped by the mortar, respectively. The van del Waals force is the main molecular effect of interface adhesion, and both acidic and alkaline aggregate components significantly tend to form hydrogen bonds with water rather than asphalt, thus attenuating the interface adhesion. Full article

Deicing salt effectively melts ice and snow to maintain traffic flow in seasonal freezing zones, but its erosion effect compromises the water stability and structural integrity of asphalt pavements. To comprehensively explore the impacts of salt erosion on the interfacial behaviors of rubber asphalt–aggregate systems, this study developed a multiscale characterization method integrating a macroscopic mechanical test, microscopic tests, and molecular dynamics (MD) simulations. Firstly, laboratory-controlled salt–freeze–thaw cycles were employed to simulate field conditions, followed by quantitative evaluation of interfacial bonding properties through pull-out tests. Subsequently, the atomic force microscopy (AFM) and Fourier transform infrared spectrometer (FTIR) tests were conducted to characterize the microscopic morphology evolution and chemical functional group transformations, respectively. Moreover, by combining the diffusion coefficients of water molecules, salt solution ions, and asphalt components, the mechanism of interfacial salt erosion was elucidated. The results demonstrate that increasing NaCl concentration and freeze–thaw cycles progressively reduces interfacial pull-out strength and fracture energy, with NaCl-induced damage becoming limited after twelve salt–freeze–thaw cycles. In detail, with exposure to 15 freeze–thaw cycles in 6% NaCl solution, the pull-out strength and fracture energy of the rubber asphalt–limestone aggregate decrease by 50.47% and 51.57%, respectively. At this stage, rubber asphalt exhibits 65.42% and 52.34% increases in carbonyl and sulfoxide indexes, respectively, contrasted by 49.24% and 42.5% decreases in aromatic and aliphatic indexes. Long-term exposure to salt–freeze–thaw conditions promotes phase homogenization, ultimately reducing surface roughness and causing rubber asphalt to resemble matrix asphalt morphologically. At the rubber asphalt–NaCl solution–aggregate interface, the diffusion of Na⁺ is faster than that of Cl⁻. Meanwhile, compared with other asphalt components, saturates exhibit notably enhanced mobility under salt erosion conditions. The synergistic effects of accelerated aging, salt crystallization pressure, and enhanced ionic diffusion jointly induce the deterioration of interfacial bonding, which accounts for the decrease in macroscopic pull-out strength. This multiscale investigation advances understanding of salt-induced deterioration while providing practical insights for developing durable asphalt mixtures in cold regions. Full article

Nowadays, the problems of traffic accidents, inefficiency, and congestion still affect transportation systems. Conventional solutions often do not resolve and can even exacerbate the problems. Intelligent transportation system (ITS) technology, including intelligent vehicles, could provide a solution for these problems. However, such technologies should be thoroughly verified and validated before their large-scale adoption. Computer simulation can be used for this task to avoid the expenses of real-world testing. Modern consumer hardware computers are not powerful enough to handle large-scale scenes with high detail. Therefore, a parallel simulation approach employing multiple computers, each processing a separate scene of limited size, is proposed. To define the requirements for a suitable simulation tool, the needs of ITS simulation and Digital Twin technology are discussed, and existing simulation environments suitable for ITS technology verification and validation are evaluated. Further, an architecture for a parallel and multi-level simulation environment for large-scale detailed ITS modeling is proposed. The proposed integrated simulation environment uses the nanoscopic CARLA and microscopic SUMO simulators to implement multi-level and parallel nanoscopic simulation by creating a large scene on the microscopic simulation level and combining the information from multiple parallelly executed nanoscopic scenes. Special handling for nanoscopic scene logic is proposed using a concept of Buffer Zones that allows traffic participants to perceive environmental information beyond the logical boundary of the scene they belong to. The proposed approaches are demonstrated in a series of experiments as a proof of concept and are integrated into the CAVISE simulation environment. Full article

This paper addresses the modeling challenge of significant asymmetry between acceleration and deceleration processes in car-following behavior by proposing an Asymmetric Acceleration and Deceleration Car Following (AAD-CF) model. The model characterizes driving decisions using both desired speed and desired spacing, and incorporates an asymmetric correction factor to capture differences in acceleration and deceleration behavior. Based on real vehicle trajectory data from the I-80 dataset, the model was compared at the microscopic level against classical models such as Gipps in terms of trajectory fitting error. The results show that the AAD-CF model consistently achieves lower trajectory fitting errors across different simulation time-steps, with error reduction exceeding 10%. At the macroscopic traffic flow level, the model successfully reproduced three-phase traffic flow states—free flow, synchronized flow, and wide moving jams. By implementing both startup and emergency braking scenarios, it was further revealed that braking waves propagate approximately 40% faster than startup waves, demonstrating asymmetric wave propagation. This study provides quantitative evidence for understanding the intrinsic relationship between microscopic driving behavior and macroscopic traffic phenomena, and the proposed model can support traffic simulation systems and theoretical analysis. Full article

Traffic simulation is essential for evaluating transportation policies, infrastructure changes, and mixed traffic scenarios with human-driven and autonomous vehicles, yet its reliability critically depends on accurate calibration of origin–destination (OD) traffic demand. Traditional calibration workflows rely on trial-and-error adjustments and static numerical outputs, making it difficult for analysts to interpret error patterns, understand OD-to-link relationships, and efficiently refine traffic demand in complex urban networks. To address these challenges, we conducted a design study with transportation simulation experts to characterize the OD calibration workflow and derive key analytical tasks, which informed the development of ODCalibrator, an interactive visualization system that supports human-in-the-loop calibration through coordinated views, visual diagnostics, and iterative adjustment capabilities. We demonstrate its utility via a narrative usage scenario and domain expert feedback, showing that the system enables analysts to quickly identify error-prone regions, explore the effects of OD adjustments, and leverage domain expertise to produce more efficient and interpretable calibration outcomes for urban traffic simulations. Full article